Expanding the donor pool: Donation after circulatory death and living liver donation do not compromise the results of liver transplantation
Bibliographic record
Abstract
Because of the shortfall between the number of patients listed for liver transplantation (LT) and the available grafts, strategies to expand the donor pool have been developed. Donation after circulatory death (DCD) and living donor (LD) grafts are not universally used because of the concerns of graft failure, biliary complications, and donor risks. In order to overcome the barriers for the implementation of using all 3 types of grafts, we compared outcomes after LT of DCD, LD, and donation after brain death (DBD) grafts. Patients who received a LD, DCD, or DBD liver graft at the University of Toronto were included. Between January 2009 through April 2017, 1054 patients received a LT at our center. Of these, 77 patients received a DCD graft (DCD group); 271 received a LD graft (LD group); and 706 received a DBD graft (DBD group). Overall biliary complications were higher in the LD group (11.8%) compared with the DCD group (5.2%) and the DBD group (4.8%; P < 0.001). The 1-, 3-, and 5-year graft survival rates were similar between the groups with 88.3%, 83.2%, and 69.2% in the DCD group versus 92.6%, 85.4%, and 84.7% in the LD group versus 90.2%, 84.2%, and 79.9% in the DBD group (P = 0.24). Furthermore, the 1-, 3-, and 5-year patient survival was comparable, with 92.2%, 85.4%, and 71.6% in the DCD group versus 95.2%, 88.8%, and 88.8% in the LD group versus 93.1%, 87.5%, and 83% in the DBD group (P = 0.14). Multivariate Cox regression analysis revealed that the type of graft did not impact graft survival. In conclusion, DCD, LD, and DBD grafts have similar longterm graft survival rates. Increasing the use of LD and DCD grafts may improve access to LT without affecting graft survival rates. Liver Transplantation 24 779-789 2018 AASLD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".